- Strong production experience on contemporary data platforms such as Databricks, Snowflake, BigQuery, cloud data lakes, lakehouses, or equivalent enterprise data platforms.

- Deep working knowledge of Python, SQL, PySpark, Apache Spark, and modern data pipeline development practices.
- Hands-on experience with both structured and unstructured data ingestion at enterprise scale.
- Strong experience in building pipelines for enterprise sources such as ERP, CRM, OSS/BSS, billing systems, finance systems, ServiceNow, Salesforce, SAP, Oracle, and legacy databases.
- Working knowledge of vector databases such as Pinecone, Weaviate, pgvector, Milvus, Chroma, or equivalent technologies.
- Hands-on knowledge of knowledge graphs, graph data modelling, graph querying, and enterprise graph implementation using Neo4j, Cypher, RDF, OWL, or equivalent technologies.
- Design production-grade enterprise connectors and ETL/ELT pipelines for both structured enterprise systems such as ERP, CRM, OSS/BSS, billing, finance, HR, and unstructured sources such as emails, documents, logs, transcripts, and media files.
- Build ingestion and transformation pipelines using Python, SQL, PySpark, Apache Spark, Airflow, dbt, Dagster, Flink, or equivalent technologies.




- Create frameworks for data labelling, contextualization, harmonization, enrichment, and classification workflows to configure AI agents.
- Architect integration with knowledge graphs and vector databases for hybrid search, semantic retrieval, contextual reasoning, and AI-ready data access.
- Build and maintain Ontology/knowledge graph pipelines using Neo4j, RDF/OWL, Apache Jena, Stardog, GraphDB, or equivalent technologies.
- Implement graph validation frameworks such as SHACL or ShEx to programmatically enforce data integrity rules over enterprise knowledge graphs.
- Implement data quality automation using frameworks such as Great Expectations, AWS Glue DataBrew, dbt tests, custom validation pipelines, or equivalent tools.
- Exposure to telecom, BFSI, manufacturing, or other complex enterprise domains.
- Experience with OSS/BSS, ERP, CRM, billing, order management, product catalog, service inventory, or network inventory systems.
- Experience with RDF triple stores such as Apache Jena, Stardog, GraphDB, Amazon Neptune, or equivalent technologies.
- Experience with data catalogues, metadata management tools, lineage platforms, or governance platforms.

📌 AI Data Architect / Senior Data Engineer - DaAI (Bengaluru)
🏢 Infosys
📍 Bengaluru